Current best version

Figure 1 - This figures shows…

Figure 1 - This figures shows…

Code

  1. R environment setup
  2. Setting time breaks
  3. Defining origins
  4. Import raster data
  5. GAM smoothing models
  6. Compare rates between origins and not-origins
  7. Compare rates between different time periods
  8. Setup final figure
  9. Map for final figure
  10. Trend through time panel for final figure
  11. Assemble and print final figure

R environment setup

Attach libraries

library(png)
library(maptools)
Checking rgeos availability: TRUE
library(raster)
library(gam)
Loading required package: splines
Loading required package: foreach
foreach: simple, scalable parallel programming from Revolution Analytics
Use Revolution R for scalability, fault tolerance and more.
http://www.revolutionanalytics.com
Loaded gam 1.14

Set working directory

setwd("~/Desktop/Botero postdoc 2016/Human density and the origins of agriculture/")

Setting time breaks

Define the times of agricultural origins

par(mar=c(0,0,0,20))
d <- readPNG("Larson_dates.png")
plot(seq(0,18, length.out = 19), seq(0,36, length.out = 19), type="n",ylim=c(0,36),xlim=c(0, 18), xaxt="n")
rasterImage(d, 0,0,18,36, interpolate=TRUE, col=d)
Start_of_early_window <- 16-12
End_of_early_window_start_of_late_window <- 8.2
End_of_late_window <- 17-4.2
polygon(x=c(Start_of_early_window, Start_of_early_window, End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window), y=c(0, 34, 34, 0), col=adjustcolor("limegreen", alpha= 0.2), border=adjustcolor("limegreen", alpha= 0.9))
polygon(x=c( End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window, End_of_late_window, End_of_late_window), y=c(0, 34, 34, 0), col=adjustcolor("firebrick", alpha= 0.2), border=adjustcolor("firebrick", alpha= 0.9))

These dates are provided in the supplimentary information for the Larson (2014) paper. I’ve copied those values into a .csv table provided here.

domestication_times <- read.csv("Domestication timing larson 2014.csv")
dim(domestication_times)
[1] 77  8
Region Species Start.Exploitation Finish.Exploitation Start.predomestication Finish.predomestication Start.Domestication Finish.Domestication
Southwest asia Wheat 12.00 11.25 11.25 11.00 11.00 9.00
Southwest asia Barley 12.00 11.25 11.25 10.50 10.50 9.00
Southwest asia Lentil 12.00 11.00 11.00 10.50 10.50 9.00
Southwest asia Pea 11.50 11.00 11.00 10.00 10.00 8.50
Southwest asia Chickpea 11.00 10.50 10.50 10.25 10.25 8.25
Southwest asia Broadbean NA NA NA NA 10.50 NA
Southwest asia Flax 12.00 9.50 NA NA 9.50 NA
Southwest asia Olive 10.00 6.00 NA NA 6.00 NA
Southwest asia Sheep 12.00 10.50 10.50 9.75 9.75 8.00
Southwest asia Goat 12.00 10.50 10.50 9.75 9.75 8.00
Southwest asia Pig 12.00 11.50 11.50 9.75 10.25 9.00
Southwest asia Cattle, taurine 11.50 10.50 10.50 10.25 10.25 8.00
Southwest asia Cat NA NA 10.50 4.00 4.00 NA
South Asia Tree cotton 8.50 4.50 NA NA 4.50 NA
South Asia Rice 8.00 5.00 5.00 4.00 4.00 2.50
South Asia Little millet NA NA NA NA 4.50 NA
South Asia Browntop millet NA NA NA NA 4.00 NA
South Asia Mungbean NA NA 4.50 3.50 3.50 3.00
South Asia Pigeonpea NA NA NA NA 3.50 NA
South Asia Zebu cattle 9.00 8.00 NA NA 8.00 6.50
South Asia Water buffalo 6.00 4.50 NA NA 4.50 NA
East Asia Broomcorn Millet 10.00 8.00 NA NA 8.00 NA
East Asia Foxtail millet 11.50 7.50 NA NA 7.50 NA
East Asia Rice 10.00 8.00 8.00 7.50 7.50 5.00
East Asia Soybean 8.50 5.50 NA NA 5.50 4.00
East Asia Ramie NA NA NA NA 5.25 NA
East Asia Melon 7.00 4.00 NA NA 4.00 3.75
East Asia Pig 12.00 8.50 NA NA 8.50 6.00
East Asia Silkworm 7.00 5.25 NA NA 5.25 NA
East Asia Yak NA NA NA NA 4.25 NA
East Asia Horse 7.50 6.75 6.75 5.50 5.50 4.00
East Asia Bactrian Camel NA NA NA NA 4.50 NA
East Asia Duck 2.50 1.00 NA NA 1.00 NA
East Asia Chicken 6.00 4.00 NA NA 4.00 NA
New Guinea Banana 10.00 7.00 7.00 4.00 4.00 NA
New Guinea Taro 10.00 7.00 7.00 4.00 NA NA
New Guinea Yam 10.00 7.00 7.00 4.00 NA NA
Africa and Arabia Date palm 7.00 6.00 NA NA 5.00 NA
Africa and Arabia Sorghum 8.00 4.00 NA NA 4.00 NA
Africa and Arabia Pearl millet NA NA NA NA 4.50 3.50
Africa and Arabia Fonio NA NA NA NA 2.50 NA
Africa and Arabia Cowpea NA NA NA NA 3.75 NA
Africa and Arabia Hyacinth bean NA NA NA NA 3.75 NA
Africa and Arabia Rice 3.50 2.00 NA NA 2.00 NA
Africa and Arabia Oil palm 9.25 3.50 NA NA 3.50 NA
Africa and Arabia Cattle NA NA 9.00 7.75 7.75 6.50
Africa and Arabia Donkey 9.00 5.50 NA NA 5.50 3.50
Africa and Arabia Dromedary camel 6.50 3.00 NA NA 3.00 NA
Africa and Arabia Guinea fowl NA NA 2.50 1.50 1.50 NA
North America Squash 6.50 5.00 NA NA 5.00 NA
North America Sunflower 6.00 4.75 NA NA 4.00 NA
North America Sumpweed 6.00 4.50 NA NA 4.00 NA
North America Pitseed goosefoot 4.75 3.75 NA NA 3.75 NA
Meso-america Squash (pepo) NA NA NA NA 10.00 9.50
Meso-america Maize 10.00 9.00 NA NA 9.00 NA
Meso-america Foxtail millet-grass NA NA NA NA 6.00 4.00
Meso-america Common bean NA NA NA NA 3.00 NA
Meso-america Avocado NA NA NA NA 3.00 NA
Meso-america Chile pepper NA NA NA NA 3.00 NA
Meso-america Turkey NA NA NA NA 2.00 NA
South America Chili pepper NA NA NA NA 6.00 NA
South America Peanut NA NA NA NA 5.00 NA
South America Cotton NA NA NA NA 6.00 NA
South America Coca NA NA NA NA 8.00 NA
South America Now-minor root crops (arrowroot, leren) NA NA NA NA 9.00 NA
South America Squash NA NA NA NA 10.00 NA
South America Common bean NA NA NA NA 5.00 NA
South America Lima bean NA NA 8.25 NA 6.00 NA
South America Monioc NA NA NA NA 7.00 NA
South America Sweet potato NA NA NA NA 5.00 NA
South America White potato 7.00 4.50 NA NA 4.00 NA
South America Quinoa 5.00 NA NA NA 3.50 NA
South America Yam NA NA NA NA 5.50 NA
South America Llama 10.00 6.00 NA NA 6.00 NA
South America Alpaca 10.00 5.00 NA NA 5.00 NA
South America Guinea pig NA NA NA NA 5.00 NA
South America Muscovy Duck NA NA NA NA 4.00 NA
par(mar=c(5,4,6,1))
dates <- unlist(domestication_times[3:8])
hist(dates, breaks = 22, xlim=c(15,0), xlab="K years ago", col=adjustcolor("cornflowerblue", alpha= 0.5), border=adjustcolor("cornflowerblue", alpha= 0.9), main="All dates in dataset"  )
mtext("This tells us about how evenly our evidence is distributed in time", 3, line=1)

hist(dates, breaks = 22, xlim=c(15,0), xlab="Thousand years ago", col=adjustcolor("cornflowerblue", alpha= 0.5), border=adjustcolor("cornflowerblue", alpha= 0.9), main="All dates in dataset with Larson(2014) date windows")
Start_of_early_window <- 12
End_of_early_window_start_of_late_window <- 8.2
End_of_late_window <- 4.2
polygon(x=c(Start_of_early_window, Start_of_early_window, End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window), y=c(0, 30, 30, 0), col=adjustcolor("limegreen", alpha= 0.2), border=adjustcolor("limegreen", alpha= 0.9))
polygon(x=c( End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window, End_of_late_window, End_of_late_window), y=c(0, 30, 30, 0), col=adjustcolor("firebrick", alpha= 0.2), border=adjustcolor("firebrick", alpha= 0.9))
hist(dates, breaks = 22, xlim=c(15,0), xlab="K years ago", col=adjustcolor("cornflowerblue", alpha= 0.2), border=adjustcolor("cornflowerblue", alpha= 0.9), main="", add=TRUE)
mtext("Early Holocene", 3, line = -1, adj=.3)
mtext("Middle Holocene", 3, line= -1, adj=.6)

par(mfrow=c(2,3), mar=c(4,4,2,0))
specific_dates <- domestication_times[3:9]
Error in `[.data.frame`(domestication_times, 3:9) : 
  undefined columns selected

I’m creating new rows for this table, combining dates in different ways to make the CDFs below look more authentic. This makes it so that pre-ag always happens before post-ag. What I’ve done is given the later date to the earlier date when those dates are missing.

h <- which(is.na(domestication_times[,3]))
domestication_times <- cbind(domestication_times, rep(NA, length(domestication_times[,1])))
domestication_times[,9] <- domestication_times[,3]
domestication_times[h,9] <- domestication_times[h,7]
colnames(domestication_times)[9] <- "adopt exploitation date"
domestication_times[,10] <- domestication_times[,7]
domestication_times[which(is.na(domestication_times[,10])),10] <- 0
colnames(domestication_times)[10] <- "start of ag"
#save(domestication_times, file="~/Desktop/Human density and the origins of agriculture/Domestication timing larson 2014.Rdata")

I think these are best described by a cummulative distribution, showing how they accumulate over time.

for(i in 1:8){
type_number <- i
    match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 9]
    maxer <- max(match, na.rm=TRUE)
    j <- ecdf(maxer-match)
    print(levels(domestication_times$Region)[ type_number])
    print(match)
    print(j)
}
[1] "Africa and Arabia"
 [1] 7.00 8.00 4.50 2.50 3.75 3.75 3.50 9.25 7.75 9.00 6.50 1.50
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,   0.25,   1.25,  ...,   6.75,   7.75
[1] "East Asia"
 [1] 10.00 11.50 10.00  8.50  5.25  7.00 12.00  7.00  4.25  7.50  4.50  2.50  6.00
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,    0.5,      2,  ...,   7.75,    9.5
[1] "Meso-america"
[1] 10 10  6  3  3  3  2
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:4] =      0,      4,      7,      8
[1] "New Guinea"
[1] 10 10 10
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:1] =      0
[1] "North America"
[1] 6.50 6.00 6.00 4.75
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:3] =      0,    0.5,   1.75
[1] "South America"
 [1]  6.0  5.0  6.0  8.0  9.0 10.0  5.0  6.0  7.0  5.0  7.0  5.0  5.5 10.0 10.0  5.0  4.0
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:8] =      0,      1,      2,  ...,      5,      6
[1] "South Asia"
[1] 8.5 8.0 4.5 4.0 3.5 3.5 9.0 6.0
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:7] =      0,    0.5,      1,  ...,      5,    5.5
[1] "Southwest asia"
 [1] 12.0 12.0 12.0 11.5 11.0 10.5 12.0 10.0 12.0 12.0 12.0 11.5  4.0
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:6] =      0,    0.5,      1,  ...,      2,      8
par(mfcol=c(2,5), mar=c(4,0,5,0))
plot(0,0, type="n", xaxt="n", xlab="", bty="n")
mtext("Percent of species that will eventually \n be domesticated in a region", 2, line=-5, cex=0.5)
plot(0,0, type="n", xaxt="n", xlab="", bty="n")
mtext("Percent of species that will eventually \n be domesticated in a region", 2, line=-5, cex=0.5)
for(i in 1:8){
type_number <- i
    match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 9]
    maxer <- max(match, na.rm=TRUE)
    j <- ecdf(maxer-match)
    #print(j)
    
plot(0,0, xlim=c(15,0), ylim=c(0,100), ylab="Percent of species that will eventually \n be domesticated in a region", xlab="Thousand years ago", main=levels(domestication_times$Region)[ type_number], type="n", yaxt="n")
x_seq <- rev(c(0,seq(0, maxer, length.out=100)))
y_seq <- 100 * (c(0, j(seq(0, maxer, length.out=100))))
lines(x_seq, y_seq,  ylim=c(-1,1))
polygon(c(0, x_seq), c(0, y_seq), border=adjustcolor("cornflowerblue",alpha=1), col=adjustcolor("cornflowerblue", alpha=0.2))
if(i == 2 | i == 1)axis(2)
if(i == 3)mtext("Cummulative distribution function for the accumulation of domesticates", 3, line=3.8, col="cornflowerblue")
}

par(mfcol=c(2,5), mar=c(4,0,5,0))
plot(0,0, type="n", xaxt="n", xlab="", bty="n")
mtext("Percent of species that will eventually \n be domesticated in a region", 2, line=-5, cex=0.5)
plot(0,0, type="n", xaxt="n", xlab="", bty="n")
mtext("Percent of species that will eventually \n be domesticated in a region", 2, line=-5, cex=0.5)
for(i in 1:8){
type_number <- i
    match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 9]
    maxer <- max(match, na.rm=TRUE)
    j <- ecdf(maxer-match)
    #print(j)
    
plot(0,0, xlim=c(15,0), ylim=c(0,100), ylab="Percent of species that will eventually \n be domesticated in a region", xlab="Thousand years ago", main=levels(domestication_times$Region)[ type_number], type="n", yaxt="n")
x_seq <- rev(c(0,seq(0, maxer, length.out=100)))
y_seq <- 100 * (c(0, j(seq(0, maxer, length.out=100))))
lines(x_seq, y_seq,  ylim=c(-1,1))
polygon(c(0, x_seq), c(0, y_seq), border=adjustcolor("cornflowerblue",alpha=1), col=adjustcolor("cornflowerblue", alpha=0.2))
abline(v= maxer - quantile(j)[2], col="limegreen", lwd=2)
if(i == 2 | i == 1)axis(2)
if(i == 2)mtext("25%", 3, line=3.5, adj=-1, col="limegreen")
if(i == 3)mtext("Cummulative distribution function for the accumulation of domesticates", 3, line=3.8, col="cornflowerblue")
if(i == 4)mtext("Choose a y to predict an x", 3, line=3.3, col="cornflowerblue")
    break_one <- maxer
            break_two <- maxer - quantile(j)[2]
                
    polygon(x=c(break_two, break_two, break_one, break_one), y=c(0, 1, 1, 0), col=adjustcolor("cornflowerblue", alpha=0.2), border=adjustcolor("cornflowerblue",alpha=1))
            lines(x=c(break_two, break_two), y=c(0,-1), col="cornflowerblue")
            abline(h = 25, col="limegreen", lwd=2)
}

Make this a function. There is a choice of two methods here. At the end of this section we need to print the desision we’re passing to the later analyses.

Defining origins

origins
class       : SpatialPolygonsDataFrame 
features    : 20 
extent      : -104.7263, 144.9554, -26.31802, 43.00233  (xmin, xmax, ymin, ymax)
coord. ref. : NA 
variables   : 8
no non-missing arguments, returning NAno non-missing arguments, returning NAno non-missing arguments, returning NAno non-missing arguments, returning NAno non-missing arguments, returning NAno non-missing arguments, returning NA
names       :     CONTINENT, SQMI, SQKM, OID, CONTINEN_2,  SQMI_2,  SQKM_2, OID_2 
min values  :     C/S_Andes,   NA,   NA,  NA,     Europe, 3821854, 9898597,     7 
max values  : West Africa T,   NA,   NA,  NA,     Europe, 3821854, 9898597,     7 
library(maps)
map()
map(origins, add=TRUE, fill=TRUE, col=adjustcolor("cornflowerblue", alpha=1))
database does not (uniquely) contain the field 'name'.

map()
d <- readPNG("Larson_origins.png")
rasterImage(d, -180, -90, 180, 110, interpolate=TRUE, col=d)
map(add=TRUE)
map(origins, add=TRUE, fill=TRUE, col=adjustcolor("cornflowerblue", alpha=1))
database does not (uniquely) contain the field 'name'.

This is obviously a bad projection fit right now.

Import raster data

#subset and reorder origins. This is currently done at the end of the plot but should be moved forward.
# Load data for population density
load("PopD_all_December.rdata")
PopD.ALL
class       : RasterStack 
dimensions  : 288, 720, 207360, 18  (nrow, ncol, ncell, nlayers)
resolution  : 0.5, 0.5  (x, y)
extent      : -180, 180, -60, 84  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
names       :        fourK,        fiveK,         sixK,       sevenK,       eightK,        nineK,         tenK,      elevenK,      twelveK,    thirteenK,    fourteenK,     fifteenK,     sixteenK,   seventeenK,    eighteenK, ... 
min values  : 5.611358e-07, 1.067142e-06, 2.508241e-06, 6.317553e-06, 2.286934e-05, 7.631922e-05, 1.272693e-04, 2.118215e-04, 2.602175e-04, 3.226203e-04, 4.390267e-04, 5.572032e-04, 7.313966e-04, 8.286005e-04, 8.297062e-04, ... 
max values  :     2.051069,     2.013452,     2.142908,     1.888403,     1.863014,     1.880628,     1.650615,     1.678033,     1.697732,     1.499115,     1.517264,     1.443677,     1.464867,     1.453581,     1.436394, ... 
# Extract data to a matrix
Pop <- values(PopD.ALL)
r <- raster(PopD.ALL, 1)
r
class       : RasterLayer 
dimensions  : 288, 720, 207360  (nrow, ncol, ncell)
resolution  : 0.5, 0.5  (x, y)
extent      : -180, 180, -60, 84  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
data source : in memory
names       : fourK 
values      : 5.611358e-07, 2.051069  (min, max)

GAM smoothing models

Justification for General Adative Models.

We need to justify our decision to use a GAM over other models. This should include citations to back up those arguments.

Fit and plot GAM model with different degrees of freedom

We should make our decisions very transparent here. We should be able to justify our decision of 3 degrees of freedom over other possible values.

Density projections

plot(global.means[[1]], col=adjustcolor("cornflowerblue", alpha=0.8), pch=names(global.means[[1]]), type="b", xlab="year", ylab="Density", xaxt="n")
axis(1, at=seq(1,18, by=1), label=rev(seq(4, 21, by=1)))

means_matrix <- matrix(rep(NA,19*20), 20, 19)
colnames(means_matrix) <- c("origin", rev(seq(4, 21, by=1)))
means_matrix[,1] <- names(global.means)
for(i in 1:20){
means_matrix[i,2:19] <- global.means[[i]]
}
kable(means_matrix, caption= "This is our world")
origin 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4
W_African_Sav Means 0.277236125574504 0.268146096510109 0.229965375382666 0.32523944033517 0.0993713185018438 0.0037800484090062 0.0288533426383454 0.0965336556256875 0.170551592391401 0.23617134733154 0.329881362382384 0.408474454716208 0.933326290541932 1.0205068503086 0.850021182238413 0.257169818734039 0.326479019859158 0.507805632758623
Sudanic_Savan Means 0.016989310920534 0.0165830329202393 0.0137788739758862 0.0187343740600394 0.0267628737555364 0.0504019933855703 0.111853742051633 0.171175628805818 0.229766197375977 0.290372573994489 0.393633312377065 0.465128867070118 0.952417421795436 1.01093274306512 0.893099872916669 0.355788155105045 0.546151812406506 0.75726422940137
West Africa T Means 0.0189861304288692 0.0211956898670637 0.0184494598528687 0.0122957090963052 0.0401148140595455 0.0642571026136034 0.122814423606705 0.18024456411236 0.238763360681693 0.296946782277892 0.393959129288487 0.475949031004617 0.875757914548295 0.90858669240854 0.874514137796506 0.581496815464146 0.745072302084471 0.956646116404008
Ethipian plat Means 0.0904589833106248 0.0903924937521069 0.0863100536268273 0.0893963604501343 0.101029422893253 0.112809652424169 0.165444985051315 0.237587289908825 0.335763703990818 0.422484059629967 0.563949159000512 0.624066354130941 0.940018099247958 0.954579684776469 0.920354009533087 0.563097081815597 0.604720525289939 0.917811255034155
NA Means 0.663114221139142 0.660555808084869 0.642678366385832 0.68037338350395 0.579197369561157 0.449722187452827 0.292062953469214 0.193223598846692 0.132827947024298 0.111861275509966 0.0710971234608467 0.130373982857816 0.671207444822536 0.786755748449708 0.739053831844885 0.832107257992727 0.655180067894376 0.51579662144668
E_North_Ameri Means 0.00893546567365586 0.0104915755544869 0.013253014074329 0.00542980780574677 0.033277010182152 0.0676074551240852 0.151565641055132 0.252887929725881 0.364503616958045 0.473544250047383 0.693964954332778 0.767791046626426 0.692619559646708 0.69478102682856 0.698626071405891 0.419432397571308 0.858741718110438 1.00985185846655
New_Guinea Means 0.280908407324953 0.278870321026453 0.267694222403951 0.281470118449041 0.252745290593119 0.214728303639231 0.167084548728992 0.115970302717808 0.089799780729113 0.080892066918588 0.0124431847502232 0.0401293843516986 0.566581929269924 0.604979974822395 0.641520404478516 0.955830671499772 0.708491079993817 0.662407420268634
Mesoamerica Means 0.0161950005425715 0.0206245055086652 0.02143899866462 0.00934611302070087 0.0544114903050211 0.103081131118678 0.194265350885966 0.278924138424292 0.363943243683155 0.43990897393249 0.568842547084558 0.615137398815427 0.832150878517869 0.835544619667127 0.803694000400885 0.462771111989956 0.868405844942686 1.00928777965156
N_Lowland_SA Means 0.197689193698132 0.194041814243325 0.190984940424971 0.196128712506526 0.196417663969857 0.187068023549807 0.160506607282115 0.134145108737672 0.107884268995178 0.0911509383234282 0.0205928310679456 0.0459520795917419 0.596005745518906 0.629886813716808 0.656340274475672 0.950411787925309 0.712319378859971 0.762450575454787
NW_Lowland_SA Means 0.302160479847383 0.3047338767083 0.30145828256188 0.306608856132229 0.294571575771776 0.269112077717038 0.24906509989485 0.206289289884621 0.156152857530682 0.120418637914445 0.0391309645428076 0.0308172782102177 0.297940313948749 0.399158518142026 0.500237965916322 0.946354452820633 0.691804496481964 0.603912759846072
Sava_W_India Means 0.0106243834199155 0.0114152911613111 0.0161244909782402 0.00391888342167919 0.0411288944114358 0.0799118639893852 0.143474824462497 0.194472001633401 0.25896453538531 0.328023269123674 0.38501994780977 0.438200162623175 0.701906926325678 0.887863530419312 0.840257997855973 0.463636950860515 0.824692040164097 1.00602620757196
S_India Means 0.0115629583651973 0.0187752993630369 0.021723311306329 0.0070646947614955 0.0407072830692962 0.0713069789187112 0.139798008959816 0.225664612623677 0.281610839357003 0.348147871179238 0.403540116832751 0.477872217545242 0.800247790956004 0.852607258819713 0.838151682591359 0.653173598407203 0.915365428351119 1.00598449001798
Ganges_E_Indi Means 0.0113365151896897 0.0126436772421878 0.0137067008861891 0.00706800434124777 0.0340469630303124 0.0618791589942872 0.107610274242122 0.154284668909943 0.214022368644172 0.261411688761718 0.354911484688783 0.405502150971218 0.701022087868857 0.856628657667716 0.857644756926133 0.634690322099047 0.846206513504704 1.00363122170002
Chinese_loess Means 0.00985165064501957 0.0104808324437268 0.00900683865719935 0.0116119701845256 0.0118779155381701 0.0441243012860027 0.0716263584823884 0.0861013791329461 0.149418291528035 0.211519249389285 0.266988439490975 0.311227904177839 0.707572308573447 0.849429071406241 0.906202082492626 0.923045544934632 0.9916760767357 0.973623792552289
Japanese Means 0.432966078309294 0.436721275258284 0.430357801245452 0.448302080145254 0.403558960792971 0.34383950864674 0.275786181477207 0.200371550616222 0.126217675525547 0.0867652904934615 0.0131109616455113 0.0730107508281249 0.809250181854901 0.903296939451686 0.864704553202643 0.981926278231659 0.863987172963444 0.7582788509337
Lower-MiddleY Means 0.0120866286052244 0.0157503645001354 0.0141141386633795 0.0117842718312604 0.0320108118220579 0.0507118754130521 0.091891834618743 0.124873052749255 0.158608104722965 0.204520350655691 0.243958207153347 0.28747052316418 0.680749440029052 0.773753745502804 0.84134458277978 0.698341733661832 0.907809652199586 1.00418953333218
South trop ch Means 0.016713449702567 0.0174753363663615 0.0151393509017418 0.0180283696050808 0.0156823173722971 0.0122750768667892 0.038639651118046 0.0916115579684524 0.114645285272606 0.223588081432699 0.297587932561125 0.375458659406677 0.805249095005994 0.93801021743498 0.945522393929682 0.910830218399952 0.966004005587459 0.988016065147811
NA Means 0.0506689441611688 0.0523003760584072 0.0529223473123491 0.0583571740189899 0.0488909376957515 0.0393647803163041 0.0390561263869707 0.133860007449605 0.184609429162159 0.197183843402241 0.21980312514192 0.355570001144045 0.765746252662412 0.921681593973495 0.908058913268584 0.886924001187376 0.948972971310446 0.951733541434419
Southwes amaz Means 0.197814786548895 0.196624956520459 0.189937961661877 0.190641832484773 0.201114079962783 0.192405606587582 0.169432074839703 0.14150349548783 0.116741484382453 0.108871940612822 0.0637616038743224 0.0788896738782188 0.603605273568561 0.617468071130463 0.651007381887985 0.928816410800911 0.927512492742812 0.921262845255316
C/S_Andes Means 0.0401384562944843 0.0397694823632042 0.0403745098771443 0.0400938475174884 0.0598582525765649 0.0714790397599819 0.10113089957835 0.115327513375983 0.128946585768714 0.144650495307302 0.144317872008496 0.186436295057576 0.67781043506519 0.704637618405442 0.731079856196258 0.895704921957057 0.942947005554748 0.961094004592374
#global.SD
SD_matrix <- matrix(rep(NA,19*20), 20, 19)
colnames(SD_matrix) <- c("origin", rev(seq(4, 21, by=1)))
SD_matrix[,1] <- names(global.SD)
for(i in 1:20){
SD_matrix[i,2:19] <- global.SD[[i]]
}
kable(SD_matrix, caption= "SD values")
origin 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4
W_African_Sav SD 0.172699007009174 0.166224154719586 0.138678306162886 0.185441293815945 0.0518023278157856 0.0176743529543277 0.0232721845857313 0.0333644331899999 0.0354168207585815 0.035199893254038 0.0485347261747143 0.0608429760322292 0.0242495467702773 0.0125228565331159 0.030853798105179 0.0816359078903485 0.13119632975143 0.136708070596833
Sudanic_Savan SD 0.0159698059987182 0.0157324841477218 0.0130990900131222 0.0166003275140283 0.015966067525986 0.0256027817811989 0.0452389607615637 0.0568279371893465 0.0652666423235462 0.0671560859495923 0.052642065744974 0.0523704150626269 0.0124936002052098 0.0216830502557889 0.0357007985386511 0.142302424115745 0.125375351923483 0.137098220468632
West Africa T SD 0.0167321856989961 0.0157899403246079 0.0148137837237379 0.0113613562356161 0.0196995774415615 0.0275689805142787 0.0419436547568745 0.050492520331069 0.059679399772876 0.0658658041127462 0.0828859886252774 0.0699049776104795 0.0818358041657027 0.092465678209583 0.0535103005034764 0.128075013406526 0.0795441005120006 0.0675621686827204
Ethipian plat SD 0.162912188924422 0.160411599553525 0.151742223557912 0.170525258224472 0.120309000695041 0.0832108748773215 0.101796496236904 0.150037458872254 0.202589035265331 0.229238291028049 0.269645889743349 0.253345298475953 0.0447447233108725 0.0525085647718391 0.0424886599682493 0.18245585531685 0.0798520618580059 0.102304970623811
NA SD 0.317243930545136 0.315343575298247 0.308652081354488 0.324003088184895 0.293515373848188 0.258502627900925 0.203354497097551 0.152404334346264 0.095190692866469 0.0644775468495577 0.0863609463670797 0.129211809891193 0.141308178808749 0.184807201581753 0.142524730394035 0.141543601124512 0.0976259662285693 0.116035132973936
E_North_Ameri SD 0.00814519801285417 0.00851539781161795 0.00929401001777065 0.00447318912463679 0.0115087080492953 0.0156252653953385 0.0225849146016072 0.0251984491931828 0.0244453007992024 0.0227279927606796 0.0320698144632027 0.040761082463001 0.0524817338555138 0.052946139308667 0.0395648579830907 0.0370549127832752 0.0262840341483891 0.00134154275005444
New_Guinea SD 0.0821958325250514 0.0828638794671788 0.0781541364668149 0.0828088029896117 0.078094797782274 0.0717879349217425 0.053393900283989 0.0494630568718273 0.0385500079009193 0.0274721883505787 0.0115300949179493 0.00800295127290903 0.0872756613070994 0.0895534405758082 0.0562194157260793 0.00552327232079952 0.0333048005670632 0.0446051787672239
Mesoamerica SD 0.0211537849845949 0.0229393635287474 0.0166418607418158 0.0118194786555225 0.0225239907442187 0.0352868854772045 0.0613620740588326 0.0855395320159072 0.103613885886215 0.11422095117608 0.129846631792337 0.128105208673015 0.0452010183656475 0.0556118672284279 0.0482593402432222 0.113311245060815 0.0396131063123546 0.00328698466541702
N_Lowland_SA SD 0.103306550386419 0.100347039338042 0.101714165882939 0.106238235672369 0.0977489513449625 0.0897789632675473 0.0651858939863286 0.0489499923305428 0.0381362652127659 0.0288166238340547 0.0261316061651377 0.0217881798432685 0.11032994628824 0.120447165832298 0.1068666416441 0.017049009533096 0.082446683077705 0.12104668727093
NW_Lowland_SA SD 0.130739696844979 0.132907733073528 0.132009693999081 0.133691638250333 0.127524836612193 0.113005200692562 0.0698516459686315 0.0575681624179661 0.059602563376245 0.0631030747496126 0.0727156805650935 0.0667517958417757 0.0781785070725703 0.0931558488306065 0.0586358262852361 0.00580470161060384 0.0452537544732729 0.0521503662828824
Sava_W_India SD 0.00578695471476928 0.00522073368988782 0.00613849030781516 0.00183399992079732 0.00837019252888177 0.0148090191021166 0.023478907405565 0.0292024405234909 0.033611576878901 0.0497449326880249 0.0581864938492896 0.0755295896901178 0.0829532745787329 0.0496220092145389 0.0429972870472159 0.0519140268417683 0.0445401020980192 0.00772933089067814
S_India SD 0.0117624884650963 0.0157871049212158 0.0118145281046149 0.00655509962741526 0.012978839356834 0.0126344004077163 0.0179081104817061 0.0487275050572536 0.0429954709946546 0.0282450142482082 0.0357676443766461 0.0567307976706172 0.038340653395058 0.0341238206328938 0.0313877845976713 0.0261806771552853 0.0276222239818159 0.00112405830031077
Ganges_E_Indi SD 0.00999610376800777 0.0101615092874938 0.0104876775583078 0.00641609933281792 0.0147546484547988 0.0234146327223517 0.0376291526801385 0.0513518755513335 0.0641617622544108 0.0700848276080226 0.0999052869333427 0.108208542272367 0.14721968212923 0.125484765455796 0.102598416103331 0.0922877014003416 0.0938015475022164 0.00253370306633808
Chinese_loess SD 0.0104576172281714 0.00980110612844382 0.00783333956532036 0.00756518364091116 0.00944388611008931 0.0226519110393966 0.0218576927676012 0.0148955150204466 0.0392830305528864 0.0317202170873381 0.046021275348067 0.0443694154073961 0.103392912258369 0.0993849425873263 0.0574571628700919 0.0498239200365443 0.00632011800334687 0.0230005033057195
Japanese SD 0.173874867501537 0.163342130431977 0.149019749611258 0.150323444589142 0.145901292029872 0.127512093574691 0.0700152264283969 0.0551989152928249 0.05204487446896 0.0482288801166012 0.0390583134719698 0.0584408591210569 0.0944238954809108 0.0443487097006404 0.0399553629459968 0.0045816516907887 0.0343952741808961 0.0528178820446734
Lower-MiddleY SD 0.0121575718392651 0.0135189595661818 0.0124669761541667 0.0115122244783575 0.0141743122719921 0.016363557279366 0.0214366417551572 0.0324190064000509 0.0392120895886192 0.043682275223655 0.0564093787608781 0.0562184106071083 0.0508853757706017 0.0462179408583881 0.0293617386404968 0.0480271930716818 0.0408102405864344 0.0022127237876935
South trop ch SD 0.0125153746424966 0.0138055463439825 0.0121127130053405 0.01426390624128 0.0121833901400692 0.010938220481384 0.0271378184286839 0.0421478968600715 0.0493247074658243 0.068195642879872 0.0725679264428561 0.0915320496460424 0.107401688641242 0.094183651074615 0.0669832486146481 0.0647028879611437 0.0230594488830411 0.0221371524026078
NA SD 0.0653732907402712 0.0627918419611784 0.0584406887359542 0.0651529688452993 0.0483153457267308 0.0306513656864185 0.0293300217951196 0.0650620201262001 0.0878013105267807 0.100169276861391 0.129586238997577 0.135990304726632 0.0574536894989798 0.0485457552815111 0.0436063435578501 0.0935423241238113 0.0428321434521962 0.0735379607174006
Southwes amaz SD 0.109469697830144 0.112448446924183 0.11233937395215 0.114499793136335 0.0960984647019482 0.0791816380668241 0.0489776548427169 0.0451477303475569 0.0515466212624549 0.0645415033910373 0.0879754490546639 0.0853931959342598 0.0461741195930737 0.0496361553947693 0.0443919623194883 0.0770832795279368 0.047411891055166 0.0660149541648784
C/S_Andes SD 0.0669388782190815 0.0620953064144171 0.0643020118362701 0.0647332641994508 0.0606662239855845 0.0512285580525077 0.0562492133276492 0.0626717399773927 0.0642146029586656 0.0669676599338186 0.084046861778514 0.0857024389759996 0.103821838349181 0.113118000822383 0.0901022957978736 0.0547403879595825 0.0711151774461137 0.0737932864833159
par(mfrow=c(5,4), mar=c(0,0,0,0))
for(j in 1:20){
plot(means_matrix[1,2:19], col=adjustcolor("cornflowerblue", alpha=0.8), pch=names(means_matrix[1,2:19]), type="n", xlab="year", ylab="Density", xaxt="n")
lines(means_matrix[j,2:19], col=adjustcolor("cornflowerblue", alpha=0.8), pch=names(means_matrix[j,2:19]), type="b", xlab="year", ylab="Density", xaxt="n")
}
axis(1, at=seq(1,18, by=1), label=rev(seq(4, 21, by=1)))

# Get the predctions from Population_trend script
load("prediction.RData")
# Read the polygons
origins <- readShapePoly('Origins_updated.shp')
# Extract data
cells <- do.call(rbind, sapply(per.origin, subset, select = 1))
#cells
g.means <- apply(prediction[-cells, ], 2, mean, na.rm = TRUE) 
g.gams <- apply(prediction[-cells, ], 2, sd, na.rm = TRUE)
g.means2 <- apply(prediction[cells, ], 2, mean, na.rm = TRUE) 
g.gams2 <- apply(prediction[cells, ], 2, sd, na.rm = TRUE)
#pdf("Global_pop_trend_comparisson.pdf", width = 25, height = 20)
par(mar = c(5, 7, 7, 5))
plot(seq(0, 1, length.out = length(time)) ~ time, col = "white", main = "GLOBAL",
     xlim = c(21, 4), ylab = "Population Density (standardized)", 
     xlab = "Thousand of years ago", cex.lab = 1, cex.main = 1, cex.axis = 1)
down <- g.means - g.gams
up <- g.means + g.gams
lines(y = down, x = time, lty = 3, col = "gray40", lwd = 3)
lines(y = up, x = time, lty = 3, col = "gray40", lwd = 3)
lines(y = g.means, x = time, lwd = 4)
lines(y = g.means2, x = time, lwd = 3, col = "red")
down2 <- g.means2 - g.gams2
up2 <- g.means2 + g.gams2
lines(y = down2, x = time, lty = 3, col = "red", lwd = 3)
lines(y = up2, x = time, lty = 3, col = "red", lwd = 3)
polygon(cbind(c(12, 8.2, 8.2, 12, 12), c(-1, -1, 2, 2, -1)),
        col = rgb(0, 1, 0, alpha = .2), border = F)
polygon(cbind(c(8.2, 4.2, 4.2, 8.2, 8.2), c(-1, -1, 2, 2, -1)),
        col = rgb(.28, 0, .28, alpha = .2), border = F)

#dev.off()

Productivity

# Load patricks productivity PCA data
load('Productivity_ALL.RDATA')
# Load origin shapefiles
origins <- readShapePoly('Origins_updated.shp')
origin.time.region <- c(2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 
                        2, 2, 1, 2, 2, 2, 2, 2, 2, 2) # 1 = early; 2 = middle
# Extract the data
prod.origin <- extract(Productivity.ALL, origins)
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
# Mean and SD per region
means <- lapply(prod.origin, colMeans, na.rm = TRUE)
sds <- lapply(prod.origin, sd, na.rm = TRUE)
names(means) <- origins@data$CONTINENT
ymax <- max(unlist(means))
ymin <- min(unlist(means))
time <- 4:21
# Plot
#pdf("productivity.pdf", 20, 30) 
par(mfrow = c(5, 4), mar = c(2, 2, 2, 0))
for (i in 1:length(means)) {
  plot(y = means[[i]], x = time, xlim = c(21, 4), ylim = c(ymin, ymax),
       main = names(means)[i], cex.main = 1, cex.lab = 1, cex.axis = 1,
       ylab = "Productivity (PCA axis)", xlab = "Thousand of years ago (k)",
       pch = 20, lwd = 1, type = "l", 
       col = c("purple", "green")[origin.time.region[i]])
  up <- sds[[i]] + means[[i]]
  down <-  means[[i]] - sds[[i]]
  lines(up ~ time, lty = 2)
  lines(down ~ time, lty = 2)
  
}

#dev.off()

Compare rates between origins and not-origins

Compare rates between different time periods

Setup final figure

Frame in the layout

a <- layout(matrix(c(
    1, 1, 1, 1, 1, 1, 1, 1,
    3,  6, 7, 8, 9, 10, 11, 4, 
    3,  5, 5, 5, 5, 5, 5,   4, 
    3,  12, 13, 14, 15, 16, 17, 4,
    2, 2, 2, 2, 2, 2, 2, 2
    ), 5, 8, byrow=TRUE), width=c(1, 1, 1, 1, 1, 1, 1, 1), height=c(0.5, 1, 1.5, 1, 0.5))
layout.show(a)

Make blank template plots

frameplot <- function(){
    plot(21:0,rep(0, 22), xlim=c(17,4), ylim=c(0, 2.25), type="n", xaxt="n", yaxt="n", xlab="", ylab="")
}
frameplot_bottom <- function(){
    plot(21:0,rep(0, 22), xlim=c(17,4), ylim=c(-0.25, 2), type="n", xaxt="n", yaxt="n", xlab="", ylab="")
}
frameplot()

frameplot_bottom()

map for final figure

Make the map for the center panel (#5 on layout panel)

Trend through time panel

Setup the plot template for small panel plots (#6-17 on layout panel)

###################
type_number <- 8
complex_figure <- function(type_number, i, means, sds){
                        
if(i < 6)   polygon(x=c(12,12,8.2,8.2), y=c(-1,3,3,-1), col=adjustcolor("cornflowerblue", alpha=0.4), border=NA)                    
if(i > 5)   polygon(x=c(8.2,8.2,4.2,4.2), y=c(-1,3,3,-1), col=adjustcolor("limegreen", alpha=0.4), border=NA)
                                    
    match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 9]
    maxer <- max(match, na.rm=TRUE)
    j <- ecdf(maxer-match)
    print(j)
    
x_seq <- rev(c(0,seq(0, maxer, length.out=100)))
y_seq <- -c(0, j(seq(0, maxer, length.out=100)))
#lines(x_seq, y_seq, type="l", ylim=c(-1,1))
#polygon(c(0, x_seq), c(0, y_seq), border="black", col=adjustcolor("cornflowerblue", alpha=0.5))
#abline(v= maxer - quantile(j)[2])
    
    break_one_1 <- maxer
            break_two_1 <- maxer - quantile(j)[2]
                
#   polygon(x=c(break_two_1, break_two_1, break_one_1, break_one_1), y=c(0, 1, 1, 0), col=adjustcolor("cornflowerblue", alpha=0.5), border=NA)
            
    match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 10]
    maxer <- max(match, na.rm=TRUE)
    j <- ecdf(maxer-match)
    print(j)
    
x_seq <- rev(c(0,seq(0, maxer, length.out=100)))
y_seq <- 2+c(0, j(seq(0, maxer, length.out=100)))
#lines(x_seq, y_seq)
#polygon(c(0, x_seq), c(2, y_seq), border="black", col=adjustcolor("limegreen", alpha=0.5))
    
    break_one_2 <- maxer
            break_two_2 <- maxer - quantile(j)[2]
                
#   polygon(x=c(break_two_2, break_two_2, break_one_2, break_one_2), y=c(1, 2, 2, 1), col=adjustcolor("limegreen", alpha=0.5), border=NA)
            
    
        #abline(v=11)
    type <- 1
        
        if(type == 1){
    x <- c(means[[i]] , means[[i]]  + abs(sds[[i]]), means[[i]]  - abs(sds[[i]]))
    scaled <- scale(x , center=FALSE)
    meanss <- scaled[1:18]
    sdss_plus <- scaled[19:36]
    sdss_minus <- scaled[37:54]
    #abline(v=10, col="red")
    length(scaled)
    #lines(4:21, means[[i]] + sds[[i]])
    #polygon(x=c(4:21, 21:4), y=c(sdss_plus, rev(sdss_minus)), col=adjustcolor("firebrick", alpha=1), border="white")
    polygon(x=c(21:4,4:21), y=c(sdss_plus, rev(sdss_minus)), col=adjustcolor("firebrick", alpha=1), border="white") 
    }
    
    if(type == 2){
    x <- c(means[[i]] , means[[i]]  + abs(sds[[i]]), means[[i]]  - abs(sds[[i]]))
    scaled <- x + 1 #scale(x , center=FALSE)
    meanss <- scaled[1:18]
    sdss_plus <- scaled[19:36]
    sdss_minus <- scaled[37:54]
    #abline(v=10, col="red")
    length(scaled)
    #lines(4:21, means[[i]] + sds[[i]])
    polygon(x=c(4:21, 21:4), y=c(sdss_plus, rev(sdss_minus)), col=adjustcolor("firebrick", alpha=1), border="white")    
    }
if(type == 3){
    x <- c(means[[i]] , means[[i]]  + abs(sds[[i]]), means[[i]]  - abs(sds[[i]]))
    scaled <- x #scale(x , center=FALSE)
    meanss <- scaled[1:18]
    sdss_plus <- scaled[19:36]
    sdss_minus <- scaled[37:54]
    #abline(v=10, col="red")
    length(scaled)
    #lines(4:21, means[[i]] + sds[[i]])
    polygon(x=c(4:21, 21:4), y=c(sdss_plus, rev(sdss_minus)), col=adjustcolor("firebrick", alpha=1), border="white")    
    }
    
    
means_long_y <- c(1,1,1,1,1, meanss)
means_long_x <- c(0:4, 4:21)
 
            break_one <- break_one_2
            break_two <- break_two_2
        #       polygon(x=c(break_one, break_one, 22, 22), y=c(1, 2, 2, 1), col=adjustcolor("white", alpha=0.8), border=NA)
        #       polygon(x=c(break_two, break_two, break_one, break_one), y=c(1, 2, 2, 1), col=adjustcolor("white", alpha=0), border=NA)
            #   polygon(x=c(-1,-1, break_two , break_two), y=c(1.9, 3.1, 3.1, 1.9), col=adjustcolor("white", alpha=0.8), border=NA) 
                #abline(v= break_one, col="white")
                #abline(v= break_two, col="white")
                
                break_one <- break_one_1
            break_two <- break_two_1
        #       polygon(x=c(break_one, break_one, 22, 22), y=c(0, 1, 1, 0), col=adjustcolor("white", alpha=0.8), border=NA)
        #       polygon(x=c(break_two, break_two, break_one, break_one), y=c(0, 1, 1, 0), col=adjustcolor("white", alpha=0), border=NA)
            #   polygon(x=c(-1,-1, break_two , break_two), y=c(-1.1, .1, .1, -1.1), col=adjustcolor("white", alpha=0.8), border=NA) 
                #abline(v= break_one, col="white")
                #abline(v= break_two, col="white")
                
#lines(x=c(break_one_2, break_one_2), y=c(1,3), col="white")
#lines(x=c(break_one_1, break_one_1), y=c(1,-1), col="white")
#lines(x=c(break_two_2, break_two_2), y=c(1,3), col="white")
#lines(x=c(break_two_1, break_two_1), y=c(1,-1), col="white") 
#lines(4:21, meanss)
    lines(21:4, meanss)
    
}
frameplot()
complex_figure(7, 1, global.means, global.SD) 
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:7] =      0,    0.5,      1,  ...,      5,    5.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:4] =      0,    3.5,      4,    4.5
axis(1)
axis(2)

Assemble the figure

Assemble the figure

quartz(width=8, height=8)
layout(matrix(c(
    1, 1, 1, 1, 1, 1, 1, 1,
    3,  6, 7, 8, 9, 10, 11, 4, 
    3,  5, 5, 5, 5, 5, 5,   4, 
    3,  12, 13, 14, 15, 16, 17, 4,
    2, 2, 2, 2, 2, 2, 2, 2
    ), 5, 8, byrow=TRUE), width=c(1, 1, 1, 1, 1, 1, 1, 1), height=c(0.5, 1, 1.5, 1, 0.5))
par(mar=c(0,0,0,0))
# 1-4 label margins
blankplot <- function(){
    
    plot(0,0, xlim=c(4,21), ylim=c(1, 1.25), bty="n", type="n", xaxt="n", yaxt="n", xlab="", ylab="")
}
blankplot()
blankplot()
blankplot()
blankplot()
origins <- readShapePoly('Origins_updated.shp')
origin.time.region <- c(2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 
                        2, 2, 1, 2, 2, 2, 2, 2, 2, 2) # 1 = early; 2 = middle
as.character(origins$CONTINENT)
 [1] "W_African_Sav" "Sudanic_Savan" "West Africa T" "Ethipian plat" "Fertile_Cresc"
 [6] "E_North_Ameri" "New_Guinea"    "Mesoamerica"   "N_Lowland_SA"  "NW_Lowland_SA"
[11] "Sava_W_India"  "S_India"       "Ganges_E_Indi" "Chinese_loess" "Japanese"     
[16] "Lower-MiddleY" "South trop ch" "Chinese_loess" "Southwes amaz" "C/S_Andes"    
#subset_order <- c(1, 2, 3, 5, 6, 8, 9, 10, 11, 12, 17, 18)
subset_order <- c(8, 10, 9, 5, 18, 7, 6, 20, 1, 2, 13, 16)
origins_subset <- origins[subset_order,]
origins_subset$CONTINENT
 [1] Mesoamerica   NW_Lowland_SA N_Lowland_SA  Fertile_Cresc Chinese_loess New_Guinea   
 [7] E_North_Ameri C/S_Andes     W_African_Sav Sudanic_Savan Ganges_E_Indi Lower-MiddleY
19 Levels: C/S_Andes Chinese_loess E_North_Ameri Ethipian plat ... West Africa T
d <- readPNG("earth.png")
png(file=paste("40962.png",sep=""),width=2000,height=1000, bg="transparent")
par(mar=c(0,0,0,0))
plot(seq(-180, 180, length.out = 19), seq(-90, 90, length.out = 19), type="n",xlim=c(-180, 180),ylim=c(-90, 90), xaxt="n")
rasterImage(d, -180, -90, 180, 90, interpolate=TRUE, col=d)
polygon(x=c(-180,-180, 180,180), y=c(-90, 90, 90, -90), col=adjustcolor("white", alpha=0.1))
#rasterImage(d, -13.5, -13.5, 375, 375, interpolate=TRUE, col=d)
plot(origins_subset, add=TRUE, col=adjustcolor("white", alpha=.8), xaxt="n", border="white", lwd=4) #still need to reproject!!!
dev.off()
quartz 
     2 
d <- readPNG("40962.png")
dim(d)
[1] 1000 2000    4
par(mar=c(0,0,0,0))
plot(0:360,0:360,type="n",xlim=c(20,360),ylim=c(65,295), yaxt="n", xaxt="n")
rasterImage(d, -28.5, -13.5, 388, 375, interpolate=TRUE, col=d)
axis(2, label=seq(-90, 90, length.out = 19), at=seq(1, 360, length.out = 19), las=1)
mtext("latitude", 2, line=4, at=180)
abline(h=seq(1, 360, length.out = 19), col=adjustcolor("grey10", alpha= 0.4), lwd=1)
abline(h=180, col=adjustcolor("white", alpha= .5), lwd=1)
load('PopD_all_December.rdata')
# Extract the data
prod.origin <- extract(PopD.ALL, origins_subset)
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
library(matrixStats)
# Mean and SD per region
means <- lapply(prod.origin, colMeans, na.rm = TRUE)
sds <- lapply(prod.origin, colSds, na.rm = TRUE)
## new values from Bruno's GAM model (produced in script called Population_Trend_per_y.R)
means <- global.means
sds <- global.gams
names(means) <- origins_subset@data$CONTINENT
ymax <- max(unlist(means))
ymin <- min(unlist(means))
time <- 4:21
#plot(origins)
#means[[1]] +
#sds[[1]]
#scale(as.numeric(means[[1]]), center=FALSE)
name_vector <- as.character(origins_subset@data$CONTINENT)
for(i in 1:12){
    
    if(i > 6){frameplot()}else{frameplot_bottom()}
    
        ## customize polygons for each graph
    if(i == 1){ #mesoamerica  #values from Larson
        
            complex_figure(3, i, means, sds)
                
    
        }
    
    
    #########
    if(i == 2 ){ #NW lowlands SA  #values from Larson
        
        complex_figure(6, i, means, sds)
    
        }
        
        #########
    if( i == 3){ #NW lowlands SA  #values from Larson
        
        complex_figure(6, i, means, sds)
        
        }
        #########
    if(i == 4){ #Fertile crescent aka Southwest asia  #values from Larson
        
        
    complex_figure(8, i, means, sds)
                
        }
        
        #########
    if(i == 5){ #loess plateau  #values from Larson
        
        complex_figure(2, i, means, sds)
            
        }
        
        
        #########
    if(i == 6){ #new guinea  #values from Larson
        
        complex_figure(4, i, means, sds)
        
        }
#########
    if(i == 7){ #Eastern N.A.  #values from Larson
        
        complex_figure(5, i, means, sds)
        
            }
        #########
    if(i == 8){ #Andes  #values from Larson
        
        complex_figure(6, i, means, sds)
        
                }
#########
    if(i == 9){ #W. African Sav  #values from Larson
        
        complex_figure(1, i, means, sds)
        
            }
#########
    if(i == 10){ #Sudanic sav  #values from Larson
        
        complex_figure(1, i, means, sds)
        
                }
#########
    if(i == 11){ #Ganges  #values from Larson
        
        
        complex_figure(7, i, means, sds) 
        
        }
#########
    if(i == 12){ #loess  #values from Larson
        
        complex_figure(2, i, means, sds)
         
                }
        
        
        #lines(4:21, means[[i]])
        
        abline(h = 1, col=adjustcolor("forestgreen", alpha=.5), lty=2)
        
    # add axes to some locations
    if(i == 1 | i == 7){axis(2, at=seq(0,2, by=0.25), label=seq(0,2, by=0.25), las=1)}
    if(i == 6 | i == 12){axis(4, at=seq(0,2, by=0.25), label=seq(0,2, by=0.25), las=1)}
    #if(i == 6 | i == 12){axis(4, at=seq(2,3, by=0.25), label=seq(0,1, by=0.25), las=1)
    #   axis(4, at=seq(-1,0, by=0.25), label=rev(seq(0,1, by=0.25)), las=1)
    #   }
    if(i > 6){axis(1)} else{axis(3)}
    
    # add text 
    if(i < 7){polygon(x=c(-30, -30, 30, 30), y=c(-0.1, -0.5, -0.5, -0.1), col="black")
    mtext(name_vector[i], 1, line=-1.2, col="white", cex=0.5)}
    
    if(i > 6){polygon(x=c(-30, -30, 30, 30), y=c(2.1, 2.5, 2.5, 2.1), col="black")
    mtext(name_vector[i], 3, line=-1.2, col="white", cex=0.5)}
    
    # add axis labels
    if(i == 1 | i ==  7){mtext("scaled density potential", 2, line=4, at=1)}
    if(i ==  3){mtext("Thousand years before present", 3, line=3.5, at =5)}
    if(i ==  9){mtext("Thousand years before present", 1, line=3.5, at =5)
        
        }
    
}
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:4] =      0,      4,      7,      8
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:5] =      0,      1,      4,      7,      8
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:8] =      0,      1,      2,  ...,      5,      6
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:9] =      0,      1,      2,  ...,      6,    6.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:8] =      0,      1,      2,  ...,      5,      6
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:9] =      0,      1,      2,  ...,      6,    6.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:6] =      0,    0.5,      1,  ...,      2,      8
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:8] =      0,    0.5,   0.75,  ...,      5,      7
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,    0.5,      2,  ...,   7.75,    9.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:9] =      0,    0.5,      1,  ...,    4.5,    7.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:1] =      0
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:2] =      0,      4
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:3] =      0,    0.5,   1.75
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:3] =      0,      1,   1.25
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:8] =      0,      1,      2,  ...,      5,      6
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:9] =      0,      1,      2,  ...,      6,    6.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,   0.25,   1.25,  ...,   6.75,   7.75
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,   2.25,   2.75,  ...,   5.75,   6.25
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,   0.25,   1.25,  ...,   6.75,   7.75
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,   2.25,   2.75,  ...,   5.75,   6.25
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:7] =      0,    0.5,      1,  ...,      5,    5.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:4] =      0,    3.5,      4,    4.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,    0.5,      2,  ...,   7.75,    9.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:9] =      0,    0.5,      1,  ...,    4.5,    7.5
saveToPDF <- function(...) {
    d = dev.copy(pdf,...)
    dev.off(d)
}
saveToPNG <- function(...) {
    d = dev.copy(png,...)
    dev.off(d)
}
## Try them out
saveToPDF("my.pdf", height=8,width=8)
quartz 
     2 
saveToPNG("my.png", height=8, width=8, units="in", res=300)
quartz 
     2 
dev.off()
null device 
          1 
---
title: "Origins of agriculture density analysis"
author: "Ty Tuff"
date: 'project began: September 2016, document updated: `r strftime(Sys.time(), format
  = "%d %B %Y")`'
output:
  html_notebook: default
  word_document: default
---
## Current best version
![Figure 1 - This figures shows...](my.png)





## Code
1. [R environment setup](#r-environment-setup)
2. [Setting time breaks](#setting-time-breaks)
3. [Defining origins](#defining-origins)
4. [Import raster data](#import-raster-data)
5. [GAM smoothing models](#gam-smoothing-models)
6. [Compare rates between origins and not-origins](#compare-rates-between-origins-and-not-origins)
7. [Compare rates between different time periods](#compare-rates-between-different-time-periods)
8. [Setup final figure](#setup-final-figure)
9. [Map for final figure](#map-for-final-figure)
10. [Trend through time panel for final figure](#trend-through-time-panel)
11. [Assemble and print final figure](#assemble-the-figure)



## R environment setup
#### Attach libraries
```{r}
library(png)
library(maptools)
library(raster)
library(gam)
```



#### Set working directory
```{r}
setwd("~/Desktop/Botero postdoc 2016/Human density and the origins of agriculture/")
```



## Setting time breaks
#### Define the times of agricultural origins
![](Larson_dates.jpg)

```{r}
par(mar=c(0,0,0,20))
d <- readPNG("Larson_dates.png")
plot(seq(0,18, length.out = 19), seq(0,36, length.out = 19), type="n",ylim=c(0,36),xlim=c(0, 18), xaxt="n")

rasterImage(d, 0,0,18,36, interpolate=TRUE, col=d)



Start_of_early_window <- 16-12
End_of_early_window_start_of_late_window <- 8.2
End_of_late_window <- 17-4.2

polygon(x=c(Start_of_early_window, Start_of_early_window, End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window), y=c(0, 34, 34, 0), col=adjustcolor("limegreen", alpha= 0.2), border=adjustcolor("limegreen", alpha= 0.9))

polygon(x=c( End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window, End_of_late_window, End_of_late_window), y=c(0, 34, 34, 0), col=adjustcolor("firebrick", alpha= 0.2), border=adjustcolor("firebrick", alpha= 0.9))

```



These dates are provided in the supplimentary information for the Larson (2014) paper. I've copied those values into a .csv table provided here. 

```{r}
domestication_times <- read.csv("Domestication timing larson 2014.csv")

dim(domestication_times)
```

```{r, echo=FALSE}
library(knitr) 
kable(domestication_times, caption= "This is our world")
```


```{r}
par(mar=c(5,4,6,1))

dates <- unlist(domestication_times[3:8])
hist(dates, breaks = 22, xlim=c(15,0), xlab="K years ago", col=adjustcolor("cornflowerblue", alpha= 0.5), border=adjustcolor("cornflowerblue", alpha= 0.9), main="All dates in dataset"  )
mtext("This tells us about how evenly our evidence is distributed in time", 3, line=1)


```

```{r}
hist(dates, breaks = 22, xlim=c(15,0), xlab="Thousand years ago", col=adjustcolor("cornflowerblue", alpha= 0.5), border=adjustcolor("cornflowerblue", alpha= 0.9), main="All dates in dataset with Larson(2014) date windows")

Start_of_early_window <- 12
End_of_early_window_start_of_late_window <- 8.2
End_of_late_window <- 4.2

polygon(x=c(Start_of_early_window, Start_of_early_window, End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window), y=c(0, 30, 30, 0), col=adjustcolor("limegreen", alpha= 0.2), border=adjustcolor("limegreen", alpha= 0.9))

polygon(x=c( End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window, End_of_late_window, End_of_late_window), y=c(0, 30, 30, 0), col=adjustcolor("firebrick", alpha= 0.2), border=adjustcolor("firebrick", alpha= 0.9))

hist(dates, breaks = 22, xlim=c(15,0), xlab="K years ago", col=adjustcolor("cornflowerblue", alpha= 0.2), border=adjustcolor("cornflowerblue", alpha= 0.9), main="", add=TRUE)

mtext("Early Holocene", 3, line = -1, adj=.3)
mtext("Middle Holocene", 3, line= -1, adj=.6)

```

```{r}

par(mfrow=c(2,3), mar=c(4,4,2,0))
specific_dates <- domestication_times[3:9]

for(i in c(1, 3, 5, 2, 4, 6)){
hist(specific_dates[,i], breaks = 22, xlim=c(15,0), xlab="Thousand years ago", col=adjustcolor("cornflowerblue", alpha= 0.5), border=adjustcolor("cornflowerblue", alpha= 0.9), main= names(specific_dates)[i])

Start_of_early_window <- 12
End_of_early_window_start_of_late_window <- 8.2
End_of_late_window <- 4.2

polygon(x=c(Start_of_early_window, Start_of_early_window, End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window), y=c(0, 30, 30, 0), col=adjustcolor("limegreen", alpha= 0.2), border=adjustcolor("limegreen", alpha= 0.9))

polygon(x=c( End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window, End_of_late_window, End_of_late_window), y=c(0, 30, 30, 0), col=adjustcolor("firebrick", alpha= 0.2), border=adjustcolor("firebrick", alpha= 0.9))

hist(specific_dates[,i], breaks = 22, xlim=c(15,0), xlab="K years ago", col=adjustcolor("cornflowerblue", alpha= 0.2), border=adjustcolor("cornflowerblue", alpha= 0.9), main="", add=TRUE)
}
```


I'm creating new rows for this table, combining dates in different ways to make the CDFs below look more authentic. This makes it so that pre-ag always happens before post-ag. What I've done is given the later date to the earlier date when those dates are missing. 
```{r}
h <- which(is.na(domestication_times[,3]))
domestication_times <- cbind(domestication_times, rep(NA, length(domestication_times[,1])))
domestication_times[,9] <- domestication_times[,3]
domestication_times[h,9] <- domestication_times[h,7]
colnames(domestication_times)[9] <- "adopt exploitation date"
domestication_times[,10] <- domestication_times[,7]
domestication_times[which(is.na(domestication_times[,10])),10] <- 0
colnames(domestication_times)[10] <- "start of ag"
#save(domestication_times, file="~/Desktop/Human density and the origins of agriculture/Domestication timing larson 2014.Rdata")
```



I think these are best described by a cummulative distribution, showing how they accumulate over time. 

```{r}
for(i in 1:8){
type_number <- i
	match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 9]
	maxer <- max(match, na.rm=TRUE)
	j <- ecdf(maxer-match)
	print(levels(domestication_times$Region)[ type_number])
	print(match)
	print(j)
}
```



```{r}
par(mfcol=c(2,5), mar=c(4,0,5,0))

plot(0,0, type="n", xaxt="n", xlab="", bty="n")
mtext("Percent of species that will eventually \n be domesticated in a region", 2, line=-5, cex=0.5)
plot(0,0, type="n", xaxt="n", xlab="", bty="n")
mtext("Percent of species that will eventually \n be domesticated in a region", 2, line=-5, cex=0.5)

for(i in 1:8){
type_number <- i
	match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 9]
	maxer <- max(match, na.rm=TRUE)
	j <- ecdf(maxer-match)
	#print(j)
	
plot(0,0, xlim=c(15,0), ylim=c(0,100), ylab="Percent of species that will eventually \n be domesticated in a region", xlab="Thousand years ago", main=levels(domestication_times$Region)[ type_number], type="n", yaxt="n")

x_seq <- rev(c(0,seq(0, maxer, length.out=100)))
y_seq <- 100 * (c(0, j(seq(0, maxer, length.out=100))))

lines(x_seq, y_seq,  ylim=c(-1,1))
polygon(c(0, x_seq), c(0, y_seq), border=adjustcolor("cornflowerblue",alpha=1), col=adjustcolor("cornflowerblue", alpha=0.2))
if(i == 2 | i == 1)axis(2)

if(i == 3)mtext("Cummulative distribution function for the accumulation of domesticates", 3, line=3.8, col="cornflowerblue")
}


```


```{r}
par(mfcol=c(2,5), mar=c(4,0,5,0))

plot(0,0, type="n", xaxt="n", xlab="", bty="n")
mtext("Percent of species that will eventually \n be domesticated in a region", 2, line=-5, cex=0.5)

plot(0,0, type="n", xaxt="n", xlab="", bty="n")
mtext("Percent of species that will eventually \n be domesticated in a region", 2, line=-5, cex=0.5)

for(i in 1:8){
type_number <- i
	match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 9]
	maxer <- max(match, na.rm=TRUE)
	j <- ecdf(maxer-match)
	#print(j)
	
plot(0,0, xlim=c(15,0), ylim=c(0,100), ylab="Percent of species that will eventually \n be domesticated in a region", xlab="Thousand years ago", main=levels(domestication_times$Region)[ type_number], type="n", yaxt="n")

x_seq <- rev(c(0,seq(0, maxer, length.out=100)))
y_seq <- 100 * (c(0, j(seq(0, maxer, length.out=100))))

lines(x_seq, y_seq,  ylim=c(-1,1))
polygon(c(0, x_seq), c(0, y_seq), border=adjustcolor("cornflowerblue",alpha=1), col=adjustcolor("cornflowerblue", alpha=0.2))
abline(v= maxer - quantile(j)[2], col="limegreen", lwd=2)
if(i == 2 | i == 1)axis(2)
if(i == 2)mtext("25%", 3, line=3.5, adj=-1, col="limegreen")
if(i == 3)mtext("Cummulative distribution function for the accumulation of domesticates", 3, line=3.8, col="cornflowerblue")
if(i == 4)mtext("Choose a y to predict an x", 3, line=3.3, col="cornflowerblue")
	break_one <- maxer
			break_two <- maxer - quantile(j)[2]
				
	polygon(x=c(break_two, break_two, break_one, break_one), y=c(0, 1, 1, 0), col=adjustcolor("cornflowerblue", alpha=0.2), border=adjustcolor("cornflowerblue",alpha=1))
			lines(x=c(break_two, break_two), y=c(0,-1), col="cornflowerblue")
			abline(h = 25, col="limegreen", lwd=2)
}


```
Make this a function. 
There is a choice of two methods here. At the end of this section we need to print the desision we're passing to the later analyses. 






## Defining origins
![](Larson_origins.jpg)


```{r}
origins <- readShapePoly('Origins_updated.shp')

origin.time.region <- c(2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 
                        2, 2, 1, 2, 2, 2, 2, 2, 2, 2) # 1 = early; 2 = middle
as.character(origins$CONTINENT)

#subset_order <- c(1, 2, 3, 5, 6, 8, 9, 10, 11, 12, 17, 18)
subset_order <- c(8, 10, 9, 5, 18, 7, 6, 20, 1, 2, 13, 16)
origins_subset <- origins[subset_order,]
origins_subset$CONTINENT
origins_subset$name

```

```{r}
library(maps)
map()
map(origins, add=TRUE, fill=TRUE, col=adjustcolor("cornflowerblue", alpha=1))

```


```{r}
map()
d <- readPNG("Larson_origins.png")
rasterImage(d, -180, -90, 180, 110, interpolate=TRUE, col=d)
map(add=TRUE)
map(origins, add=TRUE, fill=TRUE, col=adjustcolor("cornflowerblue", alpha=1))

# need to reproject
```
This is obviously a bad projection fit right now. 


##Import raster data
```{r}
#subset and reorder origins. This is currently done at the end of the plot but should be moved forward.

# Load data for population density
load("PopD_all_December.rdata")
PopD.ALL
```

```{r}
# Extract data to a matrix
Pop <- values(PopD.ALL)
r <- raster(PopD.ALL, 1)
r
```




## GAM smoothing models
#### Justification for General Adative Models.
  We need to justify our decision to use a GAM over other models. This should include citations to back up those arguments. 


### Fit and plot GAM model with different degrees of freedom
We should make our decisions very transparent here. We should be able to justify our decision of 3 degrees of freedom over other possible values. 

#### Density projections



```{r}
# need to add a global mean, an everything but the origins mean, and a buffer around the origins mean. 

# Read the polygons
origins <- readShapePoly('Origins_updated.shp')

# Extract data
per.origin <- extract(r, origins, cellnumber = TRUE, buffer = 100000)
names(per.origin) <- origins@data[, 1]

# Function standardization
std <- function(x) {
  b <- (x - min(x)) / (max(x) - min(x))
  return(rev(b))
}

# Calculating mean and 
global.means <- global.SD <- list()

for (j in 1:length(per.origin)) {
  #print(j)
  originI <- Pop[per.origin[[j]][, 1], ]
  time <- 21:4
  originI <- na.exclude(originI)
  b <- apply(originI, 1, std)
  nJ <- nrow(originI)
  predictions <- matrix(nrow = nJ, ncol = length(time))
  colnames(predictions) <- as.character(time)
  for(i in 1:nJ) {
    
    # Need to show a gradient of these df values. 
    model <- gam(b[, i] ~ s(time, df = 15))
    col <- sample(rainbow(100), 1)
    predictions[i, ] <- predict(model)
  }
  global.means[[j]] <- apply(predictions, 2, mean) 
  global.SD[[j]] <- apply(predictions, 2, sd)
}



origin.time.region <- c(2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 
                        2, 2, 1, 2, 2, 2, 2, 2, 2, 2) # 1 = early; 2 = middle

names(global.means) <- paste(names(per.origin), "Means")
names(global.SD) <- paste(names(per.origin), "SD")




```

```{r}
plot(global.means[[1]], col=adjustcolor("cornflowerblue", alpha=0.8), pch=names(global.means[[1]]), type="b", xlab="year", ylab="Density", xaxt="n")
axis(1, at=seq(1,18, by=1), label=rev(seq(4, 21, by=1)))
```

```{r}
means_matrix <- matrix(rep(NA,19*20), 20, 19)
colnames(means_matrix) <- c("origin", rev(seq(4, 21, by=1)))
means_matrix[,1] <- names(global.means)
for(i in 1:20){
means_matrix[i,2:19] <- global.means[[i]]
}
```

```{r}
kable(means_matrix, caption= "Mean values")
```


```{r}
#global.SD

SD_matrix <- matrix(rep(NA,19*20), 20, 19)
colnames(SD_matrix) <- c("origin", rev(seq(4, 21, by=1)))
SD_matrix[,1] <- names(global.SD)
for(i in 1:20){
SD_matrix[i,2:19] <- global.SD[[i]]
}
```

```{r}
kable(SD_matrix, caption= "SD values")
```


```{r}
par(mfrow=c(5,4), mar=c(0,0,0,0))

for(j in 1:20){
plot(means_matrix[1,2:19], col=adjustcolor("cornflowerblue", alpha=0.8), pch=names(means_matrix[1,2:19]), type="n", xlab="year", ylab="Density", xaxt="n")


lines(means_matrix[j,2:19], col=adjustcolor("cornflowerblue", alpha=0.8), pch=names(means_matrix[j,2:19]), type="b", xlab="year", ylab="Density", xaxt="n")
}

axis(1, at=seq(1,18, by=1), label=rev(seq(4, 21, by=1)))

```


```{r}


# Get the predctions from Population_trend script
load("prediction.RData")
# Read the polygons
origins <- readShapePoly('Origins_updated.shp')

# Extract data

cells <- do.call(rbind, sapply(per.origin, subset, select = 1))
#cells
g.means <- apply(prediction[-cells, ], 2, mean, na.rm = TRUE) 
g.gams <- apply(prediction[-cells, ], 2, sd, na.rm = TRUE)
g.means2 <- apply(prediction[cells, ], 2, mean, na.rm = TRUE) 
g.gams2 <- apply(prediction[cells, ], 2, sd, na.rm = TRUE)

#pdf("Global_pop_trend_comparisson.pdf", width = 25, height = 20)
par(mar = c(5, 7, 7, 5))
plot(seq(0, 1, length.out = length(time)) ~ time, col = "white", main = "GLOBAL",
     xlim = c(21, 4), ylab = "Population Density (standardized)", 
     xlab = "Thousand of years ago", cex.lab = 1, cex.main = 1, cex.axis = 1)
down <- g.means - g.gams
up <- g.means + g.gams
lines(y = down, x = time, lty = 3, col = "gray40", lwd = 3)
lines(y = up, x = time, lty = 3, col = "gray40", lwd = 3)
lines(y = g.means, x = time, lwd = 4)

lines(y = g.means2, x = time, lwd = 3, col = "red")
down2 <- g.means2 - g.gams2
up2 <- g.means2 + g.gams2
lines(y = down2, x = time, lty = 3, col = "red", lwd = 3)
lines(y = up2, x = time, lty = 3, col = "red", lwd = 3)


polygon(cbind(c(12, 8.2, 8.2, 12, 12), c(-1, -1, 2, 2, -1)),
        col = rgb(0, 1, 0, alpha = .2), border = F)
polygon(cbind(c(8.2, 4.2, 4.2, 8.2, 8.2), c(-1, -1, 2, 2, -1)),
        col = rgb(.28, 0, .28, alpha = .2), border = F)
#dev.off()


```

#### Productivity
```{r}
# Load patricks productivity PCA data
load('Productivity_ALL.RDATA')

# Load origin shapefiles
origins <- readShapePoly('Origins_updated.shp')

origin.time.region <- c(2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 
                        2, 2, 1, 2, 2, 2, 2, 2, 2, 2) # 1 = early; 2 = middle


# Extract the data
prod.origin <- extract(Productivity.ALL, origins)
# Mean and SD per region
means <- lapply(prod.origin, colMeans, na.rm = TRUE)
sds <- lapply(prod.origin, sd, na.rm = TRUE)
names(means) <- origins@data$CONTINENT
ymax <- max(unlist(means))
ymin <- min(unlist(means))
time <- 4:21

# Plot
#pdf("productivity.pdf", 20, 30) 
par(mfrow = c(5, 4), mar = c(2, 2, 2, 0))
for (i in 1:length(means)) {
  plot(y = means[[i]], x = time, xlim = c(21, 4), ylim = c(ymin, ymax),
       main = names(means)[i], cex.main = 1, cex.lab = 1, cex.axis = 1,
       ylab = "Productivity (PCA axis)", xlab = "Thousand of years ago (k)",
       pch = 20, lwd = 1, type = "l", 
       col = c("purple", "green")[origin.time.region[i]])
  up <- sds[[i]] + means[[i]]
  down <-  means[[i]] - sds[[i]]
  lines(up ~ time, lty = 2)
  lines(down ~ time, lty = 2)
  
}
#dev.off()
```




##Compare rates between origins and not-origins

##Compare rates between different time periods


##Setup final figure
#### Frame in the layout
```{r}
a <- layout(matrix(c(
	1, 1, 1, 1, 1, 1, 1, 1,
	3,	6, 7, 8, 9, 10, 11,	4, 
	3,	5, 5, 5, 5, 5, 5, 	4, 
	3, 	12, 13, 14, 15, 16, 17,	4,
	2, 2, 2, 2, 2, 2, 2, 2
	), 5, 8, byrow=TRUE), width=c(1, 1, 1, 1, 1, 1, 1, 1), height=c(0.5, 1, 1.5, 1, 0.5))
layout.show(a)
```

#### Make blank template plots
```{r}
frameplot <- function(){
	plot(21:0,rep(0, 22), xlim=c(17,4), ylim=c(0, 2.25), type="n", xaxt="n", yaxt="n", xlab="", ylab="")
}

frameplot_bottom <- function(){
	plot(21:0,rep(0, 22), xlim=c(17,4), ylim=c(-0.25, 2), type="n", xaxt="n", yaxt="n", xlab="", ylab="")
}
```

```{r}
frameplot()
frameplot_bottom()
```




##map for final figure



#### Make the map for the center panel (#5 on layout panel)

```{r}
d <- readPNG("earth.png")
```

![](earth.png)

```{r}
png(file=paste("40962.png",sep=""),width=2000,height=1000, bg="transparent")
par(mar=c(0,0,0,0))
plot(seq(-180, 180, length.out = 19), seq(-90, 90, length.out = 19), type="n",xlim=c(-180, 180),ylim=c(-90, 90), xaxt="n")

rasterImage(d, -180, -90, 180, 90, interpolate=TRUE, col=d)

polygon(x=c(-180,-180, 180,180), y=c(-90, 90, 90, -90), col=adjustcolor("white", alpha=0.1))
#rasterImage(d, -13.5, -13.5, 375, 375, interpolate=TRUE, col=d)
plot(origins_subset, add=TRUE, col=adjustcolor("white", alpha=.8), xaxt="n", border="white", lwd=4) #still need to reproject!!!
dev.off()
```

![](40962.png)




##Trend through time panel

#### Setup the plot template for small panel plots (#6-17 on layout panel)
```{r}
###################

type_number <- 8

complex_figure <- function(type_number, i, means, sds){
						
if(i < 6)	polygon(x=c(12,12,8.2,8.2), y=c(-1,3,3,-1), col=adjustcolor("cornflowerblue", alpha=0.4), border=NA)					
if(i > 5)	polygon(x=c(8.2,8.2,4.2,4.2), y=c(-1,3,3,-1), col=adjustcolor("limegreen", alpha=0.4), border=NA)
									
	match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 9]
	maxer <- max(match, na.rm=TRUE)
	j <- ecdf(maxer-match)
	print(j)
	

x_seq <- rev(c(0,seq(0, maxer, length.out=100)))
y_seq <- -c(0, j(seq(0, maxer, length.out=100)))

#lines(x_seq, y_seq, type="l", ylim=c(-1,1))
#polygon(c(0, x_seq), c(0, y_seq), border="black", col=adjustcolor("cornflowerblue", alpha=0.5))
#abline(v= maxer - quantile(j)[2])

	
	break_one_1 <- maxer
			break_two_1 <- maxer - quantile(j)[2]
				
#	polygon(x=c(break_two_1, break_two_1, break_one_1, break_one_1), y=c(0, 1, 1, 0), col=adjustcolor("cornflowerblue", alpha=0.5), border=NA)
			

	match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 10]
	maxer <- max(match, na.rm=TRUE)
	j <- ecdf(maxer-match)
	print(j)
	

x_seq <- rev(c(0,seq(0, maxer, length.out=100)))
y_seq <- 2+c(0, j(seq(0, maxer, length.out=100)))

#lines(x_seq, y_seq)
#polygon(c(0, x_seq), c(2, y_seq), border="black", col=adjustcolor("limegreen", alpha=0.5))

	
	break_one_2 <- maxer
			break_two_2 <- maxer - quantile(j)[2]
				
#	polygon(x=c(break_two_2, break_two_2, break_one_2, break_one_2), y=c(1, 2, 2, 1), col=adjustcolor("limegreen", alpha=0.5), border=NA)
			
	
		#abline(v=11)
	type <- 1
		
		if(type == 1){
	x <- c(means[[i]] , means[[i]]  + abs(sds[[i]]), means[[i]]  - abs(sds[[i]]))
	scaled <- scale(x , center=FALSE)
	meanss <- scaled[1:18]
	sdss_plus <- scaled[19:36]
	sdss_minus <- scaled[37:54]
	#abline(v=10, col="red")
	length(scaled)
	#lines(4:21, means[[i]] + sds[[i]])
	#polygon(x=c(4:21, 21:4), y=c(sdss_plus, rev(sdss_minus)), col=adjustcolor("firebrick", alpha=1), border="white")
	polygon(x=c(21:4,4:21), y=c(sdss_plus, rev(sdss_minus)), col=adjustcolor("firebrick", alpha=1), border="white")	
	}
	
	if(type == 2){
	x <- c(means[[i]] , means[[i]]  + abs(sds[[i]]), means[[i]]  - abs(sds[[i]]))
	scaled <- x + 1 #scale(x , center=FALSE)
	meanss <- scaled[1:18]
	sdss_plus <- scaled[19:36]
	sdss_minus <- scaled[37:54]
	#abline(v=10, col="red")
	length(scaled)
	#lines(4:21, means[[i]] + sds[[i]])
	polygon(x=c(4:21, 21:4), y=c(sdss_plus, rev(sdss_minus)), col=adjustcolor("firebrick", alpha=1), border="white")	
	}

if(type == 3){
	x <- c(means[[i]] , means[[i]]  + abs(sds[[i]]), means[[i]]  - abs(sds[[i]]))
	scaled <- x #scale(x , center=FALSE)
	meanss <- scaled[1:18]
	sdss_plus <- scaled[19:36]
	sdss_minus <- scaled[37:54]
	#abline(v=10, col="red")
	length(scaled)
	#lines(4:21, means[[i]] + sds[[i]])
	polygon(x=c(4:21, 21:4), y=c(sdss_plus, rev(sdss_minus)), col=adjustcolor("firebrick", alpha=1), border="white")	
	}

	
	
means_long_y <- c(1,1,1,1,1, meanss)
means_long_x <- c(0:4, 4:21)
 
			break_one <- break_one_2
			break_two <- break_two_2
		#		polygon(x=c(break_one, break_one, 22, 22), y=c(1, 2, 2, 1), col=adjustcolor("white", alpha=0.8), border=NA)
		#		polygon(x=c(break_two, break_two, break_one, break_one), y=c(1, 2, 2, 1), col=adjustcolor("white", alpha=0), border=NA)
			#	polygon(x=c(-1,-1, break_two , break_two), y=c(1.9, 3.1, 3.1, 1.9), col=adjustcolor("white", alpha=0.8), border=NA)	
				#abline(v= break_one, col="white")
				#abline(v= break_two, col="white")
				
				break_one <- break_one_1
			break_two <- break_two_1
		#		polygon(x=c(break_one, break_one, 22, 22), y=c(0, 1, 1, 0), col=adjustcolor("white", alpha=0.8), border=NA)
		#		polygon(x=c(break_two, break_two, break_one, break_one), y=c(0, 1, 1, 0), col=adjustcolor("white", alpha=0), border=NA)
			#	polygon(x=c(-1,-1, break_two , break_two), y=c(-1.1, .1, .1, -1.1), col=adjustcolor("white", alpha=0.8), border=NA)	
				#abline(v= break_one, col="white")
				#abline(v= break_two, col="white")
				
#lines(x=c(break_one_2, break_one_2), y=c(1,3), col="white")
#lines(x=c(break_one_1, break_one_1), y=c(1,-1), col="white")
#lines(x=c(break_two_2, break_two_2), y=c(1,3), col="white")
#lines(x=c(break_two_1, break_two_1), y=c(1,-1), col="white") 
#lines(4:21, meanss)
	lines(21:4, meanss)
	
}


```

```{r}
frameplot()
complex_figure(7, 1, global.means, global.SD) 
axis(1)
axis(2)
```


##Assemble the figure
#### Assemble the figure
```{r}
quartz(width=8, height=8)

layout(matrix(c(
	1, 1, 1, 1, 1, 1, 1, 1,
	3,	6, 7, 8, 9, 10, 11,	4, 
	3,	5, 5, 5, 5, 5, 5, 	4, 
	3, 	12, 13, 14, 15, 16, 17,	4,
	2, 2, 2, 2, 2, 2, 2, 2
	), 5, 8, byrow=TRUE), width=c(1, 1, 1, 1, 1, 1, 1, 1), height=c(0.5, 1, 1.5, 1, 0.5))


par(mar=c(0,0,0,0))

# 1-4 label margins
blankplot <- function(){
	
	plot(0,0, xlim=c(4,21), ylim=c(1, 1.25), bty="n", type="n", xaxt="n", yaxt="n", xlab="", ylab="")
}

blankplot()
blankplot()
blankplot()
blankplot()





origins <- readShapePoly('Origins_updated.shp')

origin.time.region <- c(2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 
                        2, 2, 1, 2, 2, 2, 2, 2, 2, 2) # 1 = early; 2 = middle
as.character(origins$CONTINENT)

#subset_order <- c(1, 2, 3, 5, 6, 8, 9, 10, 11, 12, 17, 18)
subset_order <- c(8, 10, 9, 5, 18, 7, 6, 20, 1, 2, 13, 16)
origins_subset <- origins[subset_order,]
origins_subset$CONTINENT



d <- readPNG("earth.png")
png(file=paste("40962.png",sep=""),width=2000,height=1000, bg="transparent")
par(mar=c(0,0,0,0))
plot(seq(-180, 180, length.out = 19), seq(-90, 90, length.out = 19), type="n",xlim=c(-180, 180),ylim=c(-90, 90), xaxt="n")

rasterImage(d, -180, -90, 180, 90, interpolate=TRUE, col=d)

polygon(x=c(-180,-180, 180,180), y=c(-90, 90, 90, -90), col=adjustcolor("white", alpha=0.1))
#rasterImage(d, -13.5, -13.5, 375, 375, interpolate=TRUE, col=d)
plot(origins_subset, add=TRUE, col=adjustcolor("white", alpha=.8), xaxt="n", border="white", lwd=4) #still need to reproject!!!
dev.off()

d <- readPNG("40962.png")
dim(d)
par(mar=c(0,0,0,0))
plot(0:360,0:360,type="n",xlim=c(20,360),ylim=c(65,295), yaxt="n", xaxt="n")
rasterImage(d, -28.5, -13.5, 388, 375, interpolate=TRUE, col=d)
axis(2, label=seq(-90, 90, length.out = 19), at=seq(1, 360, length.out = 19), las=1)
mtext("latitude", 2, line=4, at=180)
abline(h=seq(1, 360, length.out = 19), col=adjustcolor("grey10", alpha= 0.4), lwd=1)
abline(h=180, col=adjustcolor("white", alpha= .5), lwd=1)


load('PopD_all_December.rdata')

# Extract the data
prod.origin <- extract(PopD.ALL, origins_subset)

library(matrixStats)
# Mean and SD per region
means <- lapply(prod.origin, colMeans, na.rm = TRUE)
sds <- lapply(prod.origin, colSds, na.rm = TRUE)

## new values from Bruno's GAM model (produced in script called Population_Trend_per_y.R)
means <- global.means
sds <- global.gams

names(means) <- origins_subset@data$CONTINENT
ymax <- max(unlist(means))
ymin <- min(unlist(means))
time <- 4:21
#plot(origins)
#means[[1]] +
#sds[[1]]
#scale(as.numeric(means[[1]]), center=FALSE)

name_vector <- as.character(origins_subset@data$CONTINENT)



for(i in 1:12){

	

	if(i > 6){frameplot()}else{frameplot_bottom()}

	
		## customize polygons for each graph
	if(i == 1){ #mesoamerica  #values from Larson
		
			complex_figure(3, i, means, sds)
				
	
		}
	
	
	#########
	if(i == 2 ){ #NW lowlands SA  #values from Larson
		
		complex_figure(6, i, means, sds)
	

		}
		
		#########
	if( i == 3){ #NW lowlands SA  #values from Larson
		
		complex_figure(6, i, means, sds)
		
		}


		#########
	if(i == 4){ #Fertile crescent aka Southwest asia  #values from Larson
		
		
	complex_figure(8, i, means, sds)
				
		}
		
		#########
	if(i == 5){ #loess plateau  #values from Larson
		
		complex_figure(2, i, means, sds)
			
		}
		
		
		#########
	if(i == 6){ #new guinea  #values from Larson
		
		complex_figure(4, i, means, sds)
		
		}


#########
	if(i == 7){ #Eastern N.A.  #values from Larson
		
		complex_figure(5, i, means, sds)
		
			}


		#########
	if(i == 8){ #Andes  #values from Larson
		
		complex_figure(6, i, means, sds)
		
				}


#########
	if(i == 9){ #W. African Sav  #values from Larson
		
		complex_figure(1, i, means, sds)
		
			}


#########
	if(i == 10){ #Sudanic sav  #values from Larson
		
		complex_figure(1, i, means, sds)
		
				}


#########
	if(i == 11){ #Ganges  #values from Larson
		
		
		complex_figure(7, i, means, sds) 
		
		}


#########
	if(i == 12){ #loess  #values from Larson
		
		complex_figure(2, i, means, sds)
		 
		 		}

		
		
		#lines(4:21, means[[i]])
		
		abline(h = 1, col=adjustcolor("forestgreen", alpha=.5), lty=2)
		
	# add axes to some locations
	if(i == 1 | i == 7){axis(2, at=seq(0,2, by=0.25), label=seq(0,2, by=0.25), las=1)}
	if(i == 6 | i == 12){axis(4, at=seq(0,2, by=0.25), label=seq(0,2, by=0.25), las=1)}
	#if(i == 6 | i == 12){axis(4, at=seq(2,3, by=0.25), label=seq(0,1, by=0.25), las=1)
	#	axis(4, at=seq(-1,0, by=0.25), label=rev(seq(0,1, by=0.25)), las=1)
	#	}
	if(i > 6){axis(1)} else{axis(3)}

	
	# add text 
	if(i < 7){polygon(x=c(-30, -30, 30, 30), y=c(-0.1, -0.5, -0.5, -0.1), col="black")
	mtext(name_vector[i], 1, line=-1.2, col="white", cex=0.5)}
	
	if(i > 6){polygon(x=c(-30, -30, 30, 30), y=c(2.1, 2.5, 2.5, 2.1), col="black")
	mtext(name_vector[i], 3, line=-1.2, col="white", cex=0.5)}
	
	# add axis labels
	if(i == 1 | i ==  7){mtext("scaled density potential", 2, line=4, at=1)}
	if(i ==  3){mtext("Thousand years before present", 3, line=3.5, at =5)}
	if(i ==  9){mtext("Thousand years before present", 1, line=3.5, at =5)
		
		}
	
}






saveToPDF <- function(...) {
    d = dev.copy(pdf,...)
    dev.off(d)
}

saveToPNG <- function(...) {
    d = dev.copy(png,...)
    dev.off(d)
}

## Try them out

saveToPDF("my.pdf", height=8,width=8)
saveToPNG("my.png", height=8, width=8, units="in", res=300)
dev.off()



```

